摘要
大田水稻生长环境复杂,稻穗尺寸相对较小,且与叶片之间贴合并被遮挡严重,准确识别复杂田间场景中的水稻稻穗并自动统计穗数具有重要意义。为了实现对局部被叶片遮挡的小尺寸稻穗的计数,设计了一种基于生成特征金字塔的稻穗检测(Generative feature pyramid for panicle detection,GFP-PD)方法。首先,针对小尺寸稻穗在特征学习时的特征损失问题,量化分析稻穗尺寸与感受野大小的关系,通过选择合适的特征学习网络减少稻穗信息损失;其次,通过构造并融合多尺度特征金字塔来增强稻穗特征。针对稻穗特征中因叶片遮挡产生的噪声,基于生成对抗网络设计遮挡样品修复模块(Occlusion sample inpainting module,OSIM),将遮挡噪声修复为真实稻穗特征,优化遮挡稻穗的特征质量。对南粳46水稻的田间图像进行模型训练与测试,GFP-PD方法对稻穗计数的平均查全率和识别正确率为90.82%和99.05%,较Faster R-CNN算法计数结果分别提高了16.69、5.15个百分点。仅对Faster R-CNN算法构造特征金字塔,基于VGG16网络的平均查全率和识别正确率分别为87.10%和93.87%,较ZF网络分别提高3.75、1.20个百分点;进一步使用OSIM修复模型、优化稻穗特征,识别正确率由93.87%上升为99.05%。结果表明,选择适合特征学习网络和构建特征金字塔能够显著提高田间小尺寸稻穗的计数查全率;OSIM能够有效去除稻穗特征中的叶片噪声,有利于提升局部被叶片遮挡的稻穗的识别正确率。
How to assess the number of rice panicles had been one of the key ways to realize high-throughput rice breeding in the modern smart farming,for that the panicle can reflect rice yield directly.In practical in-field scenarios of rice growing,the size of panicles was relatively small while the panicles were always occluded by the leaf seriously.So,it was a challenging task to accurately identify the rice panicle in the complex field scene and automatically count the number of panicles.In order to count the small-sized rice panicles locally occluded by leaves,an automatic counting method was designed which called generative feature pyramid for panicle detection(GFP-PD)based on the feature pyramid and the generative adversarial networks.To solve the problem of feature loss in feature learning of small size rice panicles,firstly,the relationship between the size of rice panicle and receptive field was analyzed quantitatively,and then the appropriate feature learning network was selected to reduce the information loss of rice panicles;secondly,the multi-scale feature pyramid was constructed and integrated to enhance the panicle features.For the noise in the panicle feature which caused by the leaves occlusion,a feature repairing network which called occlusion sample inpainting module(OSIM)was designed to optimize the quality of features containing leaves noise by restoring the noise to the real feature of rice panicles.The model was trained and tested by the in-field rice images taken from the variety of Nanjing 46.The average panicle counting accuracy and the average panicle recognition accuracy of GFP-PD were 90.82%and 99.05%,respectively,which were 16.69 percentage points and 5.15 percentage points higher than the results of Faster R-CNN.When constructing the feature pyramid for Faster R-CNN,the average counting accuracy and recognition accuracy based on VGG16 network were 87.10%and 93.87%,respectively,which were 3.75 percentage points and 1.20 percentage points higher than ZF network.After the OSIM repairing model
作者
姜海燕
徐灿
陈尧
成永康
JIANG Haiyan;XU Can;CHEN Yao;CHENG Yongkang(College of Information Science and Technology,Nanjing Agricultural University,Nanjing 210095,China;National Engineering and Technology Center for Information Agriculture,Nanjing Agricultural University,Nanjing 210095,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2020年第9期152-162,共11页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金面上项目(31872847)
江苏省重点研发计划(现代农业)项目(BE2019383)。
关键词
水稻
稻穗数
深度学习
对抗网络
特征金字塔
rice
panicle number
deep learning
adversarial networks
feature pyramid
